Overview

Dataset statistics

Number of variables19
Number of observations8485
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory156.0 B

Variable types

Categorical7
Text4
Numeric7
DateTime1

Alerts

VoteCount is highly overall correlated with Budget and 1 other fieldsHigh correlation
Budget is highly overall correlated with VoteCount and 1 other fieldsHigh correlation
Revenue is highly overall correlated with VoteCount and 1 other fieldsHigh correlation
OriginalLanguage is highly overall correlated with North America and 1 other fieldsHigh correlation
North America is highly overall correlated with OriginalLanguageHigh correlation
Asia is highly overall correlated with OriginalLanguageHigh correlation
Oceania is highly imbalanced (83.9%)Imbalance
South America is highly imbalanced (90.7%)Imbalance
Africa is highly imbalanced (93.4%)Imbalance
Budget has 3567 (42.0%) zerosZeros
Revenue has 3212 (37.9%) zerosZeros

Reproduction

Analysis started2023-11-06 18:33:29.792249
Analysis finished2023-11-06 18:33:47.249356
Duration17.46 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

OriginalLanguage
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
1
6495 
0
1990 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8485
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 6495
76.5%
0 1990
 
23.5%

Length

2023-11-06T13:33:47.629028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T13:33:48.063331image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6495
76.5%
0 1990
 
23.5%

Most occurring characters

ValueCountFrequency (%)
1 6495
76.5%
0 1990
 
23.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8485
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6495
76.5%
0 1990
 
23.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6495
76.5%
0 1990
 
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6495
76.5%
0 1990
 
23.5%
Distinct8483
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:48.726892image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length709
Median length461
Mean length193.48262
Min length28

Characters and Unicode

Total characters1641700
Distinct characters91
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8481 ?
Unique (%)> 99.9%

Sample

1st rowcobb skilled thief commits corporate espionage infiltrating subconscious targets offered chance regain old life payment task considered impossible inception implantation another persons idea targets subconscious
2nd rownatasha romanoff also known black widow confronts darker parts ledger dangerous conspiracy ties past arises pursued force stop nothing bring natasha must deal history spy broken relationships left wake long became avenger
3rd rowset 22nd century matrix tells story computer hacker joins group underground insurgents fighting vast powerful computers rule earth
4th rowuninhabitable 22ndcentury earth outcome civil war hinges cloning brain elite soldier create robot mercenary
5th rowqueen poppy branch make surprising discovery — troll worlds beyond distinct differences create big clashes various tribes mysterious threat puts trolls across land danger poppy branch band friends must embark epic quest create harmony among feuding trolls unite certain doom
ValueCountFrequency (%)
life 1187
 
0.5%
new 1130
 
0.5%
young 1112
 
0.5%
one 1099
 
0.5%
world 957
 
0.4%
must 889
 
0.4%
two 830
 
0.4%
825
 
0.4%
family 822
 
0.4%
find 782
 
0.3%
Other values (28856) 219432
95.8%
2023-11-06T13:33:49.955021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
220580
13.4%
e 177347
 
10.8%
a 110601
 
6.7%
s 108030
 
6.6%
r 106726
 
6.5%
i 104349
 
6.4%
n 102309
 
6.2%
t 95848
 
5.8%
o 90177
 
5.5%
l 73336
 
4.5%
Other values (81) 452397
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1417562
86.3%
Space Separator 220580
 
13.4%
Decimal Number 1680
 
0.1%
Final Punctuation 913
 
0.1%
Dash Punctuation 739
 
< 0.1%
Initial Punctuation 117
 
< 0.1%
Other Punctuation 84
 
< 0.1%
Other Symbol 11
 
< 0.1%
Format 4
 
< 0.1%
Modifier Letter 3
 
< 0.1%
Other values (4) 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 177347
12.5%
a 110601
 
7.8%
s 108030
 
7.6%
r 106726
 
7.5%
i 104349
 
7.4%
n 102309
 
7.2%
t 95848
 
6.8%
o 90177
 
6.4%
l 73336
 
5.2%
d 55748
 
3.9%
Other values (49) 393091
27.7%
Decimal Number
ValueCountFrequency (%)
1 474
28.2%
0 355
21.1%
9 229
13.6%
2 147
 
8.8%
6 94
 
5.6%
7 87
 
5.2%
3 83
 
4.9%
8 79
 
4.7%
5 73
 
4.3%
4 59
 
3.5%
Final Punctuation
ValueCountFrequency (%)
825
90.4%
87
 
9.5%
» 1
 
0.1%
Initial Punctuation
ValueCountFrequency (%)
85
72.6%
31
 
26.5%
« 1
 
0.9%
Format
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Dash Punctuation
ValueCountFrequency (%)
502
67.9%
237
32.1%
Other Punctuation
ValueCountFrequency (%)
81
96.4%
3
 
3.6%
Other Symbol
ValueCountFrequency (%)
9
81.8%
® 2
 
18.2%
Nonspacing Mark
ValueCountFrequency (%)
́ 2
66.7%
̈ 1
33.3%
Space Separator
ValueCountFrequency (%)
220580
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%
Other Number
ValueCountFrequency (%)
¹ 1
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1417562
86.3%
Common 224135
 
13.7%
Inherited 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 177347
12.5%
a 110601
 
7.8%
s 108030
 
7.6%
r 106726
 
7.5%
i 104349
 
7.4%
n 102309
 
7.2%
t 95848
 
6.8%
o 90177
 
6.4%
l 73336
 
5.2%
d 55748
 
3.9%
Other values (49) 393091
27.7%
Common
ValueCountFrequency (%)
220580
98.4%
825
 
0.4%
502
 
0.2%
1 474
 
0.2%
0 355
 
0.2%
237
 
0.1%
9 229
 
0.1%
2 147
 
0.1%
6 94
 
< 0.1%
7 87
 
< 0.1%
Other values (20) 605
 
0.3%
Inherited
ValueCountFrequency (%)
́ 2
66.7%
̈ 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1639477
99.9%
Punctuation 1855
 
0.1%
None 352
 
< 0.1%
Letterlike Symbols 9
 
< 0.1%
Modifier Letters 3
 
< 0.1%
Diacriticals 3
 
< 0.1%
Alphabetic PF 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220580
13.5%
e 177347
 
10.8%
a 110601
 
6.7%
s 108030
 
6.6%
r 106726
 
6.5%
i 104349
 
6.4%
n 102309
 
6.2%
t 95848
 
5.8%
o 90177
 
5.5%
l 73336
 
4.5%
Other values (27) 450174
27.5%
Punctuation
ValueCountFrequency (%)
825
44.5%
502
27.1%
237
 
12.8%
87
 
4.7%
85
 
4.6%
81
 
4.4%
31
 
1.7%
3
 
0.2%
2
 
0.1%
1
 
0.1%
None
ValueCountFrequency (%)
é 188
53.4%
í 21
 
6.0%
á 20
 
5.7%
ō 15
 
4.3%
è 13
 
3.7%
ï 11
 
3.1%
ç 9
 
2.6%
ū 8
 
2.3%
ü 6
 
1.7%
ô 5
 
1.4%
Other values (28) 56
 
15.9%
Letterlike Symbols
ValueCountFrequency (%)
9
100.0%
Modifier Letters
ValueCountFrequency (%)
ʻ 3
100.0%
Diacriticals
ValueCountFrequency (%)
́ 2
66.7%
̈ 1
33.3%
Alphabetic PF
ValueCountFrequency (%)
1
100.0%

Popularity
Real number (ℝ)

Distinct6921
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.087977
Minimum2.5687115
Maximum4.4012163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:50.509744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2.5687115
5-th percentile2.6002571
Q12.738838
median2.9672814
Q33.3282682
95-th percentile4.0113396
Maximum4.4012163
Range1.8325048
Interquartile range (IQR)0.58943018

Descriptive statistics

Standard deviation0.43671627
Coefficient of variation (CV)0.14142471
Kurtosis0.23455808
Mean3.087977
Median Absolute Deviation (MAD)0.26648308
Skewness1.0019483
Sum26201.485
Variance0.1907211
MonotonicityDecreasing
2023-11-06T13:33:50.780392image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.781672341 6
 
0.1%
2.587538446 6
 
0.1%
2.740775506 6
 
0.1%
2.690497042 5
 
0.1%
2.579155661 4
 
< 0.1%
2.716415117 4
 
< 0.1%
2.892757798 4
 
< 0.1%
2.615203651 4
 
< 0.1%
2.584978204 4
 
< 0.1%
2.719715233 4
 
< 0.1%
Other values (6911) 8438
99.4%
ValueCountFrequency (%)
2.568711502 3
< 0.1%
2.568788134 1
 
< 0.1%
2.568864759 3
< 0.1%
2.568941379 2
< 0.1%
2.5690946 2
< 0.1%
2.569171202 2
< 0.1%
2.569247798 1
 
< 0.1%
2.569324388 2
< 0.1%
2.569477551 1
 
< 0.1%
2.569783807 2
< 0.1%
ValueCountFrequency (%)
4.401216329 1
< 0.1%
4.399596379 1
< 0.1%
4.393979909 1
< 0.1%
4.393819327 1
< 0.1%
4.393436296 1
< 0.1%
4.390862483 1
< 0.1%
4.385906598 1
< 0.1%
4.384972292 1
< 0.1%
4.384934902 1
< 0.1%
4.383575435 1
< 0.1%
Distinct5419
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
Minimum1920-02-27 00:00:00
Maximum2023-10-26 00:00:00
2023-11-06T13:33:51.085276image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:51.341734image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Title
Text

Distinct7779
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:51.905469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length73
Median length59
Mean length13.557926
Min length1

Characters and Unicode

Total characters115039
Distinct characters67
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7232 ?
Unique (%)85.2%

Sample

1st rowinception
2nd rowblack widow
3rd rowmatrix
4th rowjunge
5th rowtrolls world tour
ValueCountFrequency (%)
movie 146
 
0.8%
love 108
 
0.6%
man 93
 
0.5%
ii 92
 
0.5%
last 77
 
0.4%
one 72
 
0.4%
night 70
 
0.4%
dead 68
 
0.4%
girl 62
 
0.3%
story 61
 
0.3%
Other values (6597) 17375
95.3%
2023-11-06T13:33:52.828370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 12071
 
10.5%
9739
 
8.5%
a 9109
 
7.9%
r 8082
 
7.0%
i 7818
 
6.8%
n 7422
 
6.5%
s 7375
 
6.4%
o 7276
 
6.3%
t 6620
 
5.8%
l 5629
 
4.9%
Other values (57) 33898
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 105155
91.4%
Space Separator 9739
 
8.5%
Decimal Number 116
 
0.1%
Dash Punctuation 11
 
< 0.1%
Other Punctuation 5
 
< 0.1%
Other Number 4
 
< 0.1%
Final Punctuation 3
 
< 0.1%
Nonspacing Mark 2
 
< 0.1%
Currency Symbol 2
 
< 0.1%
Format 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12071
11.5%
a 9109
 
8.7%
r 8082
 
7.7%
i 7818
 
7.4%
n 7422
 
7.1%
s 7375
 
7.0%
o 7276
 
6.9%
t 6620
 
6.3%
l 5629
 
5.4%
d 4150
 
3.9%
Other values (33) 29603
28.2%
Decimal Number
ValueCountFrequency (%)
3 35
30.2%
1 26
22.4%
2 15
12.9%
9 10
 
8.6%
4 9
 
7.8%
0 6
 
5.2%
7 6
 
5.2%
5 4
 
3.4%
8 3
 
2.6%
6 2
 
1.7%
Other Number
ValueCountFrequency (%)
³ 2
50.0%
² 1
25.0%
1
25.0%
Other Punctuation
ValueCountFrequency (%)
¿ 2
40.0%
¡ 2
40.0%
· 1
20.0%
Dash Punctuation
ValueCountFrequency (%)
8
72.7%
3
 
27.3%
Space Separator
ValueCountFrequency (%)
9739
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̀ 2
100.0%
Currency Symbol
ValueCountFrequency (%)
¢ 2
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 105155
91.4%
Common 9882
 
8.6%
Inherited 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12071
11.5%
a 9109
 
8.7%
r 8082
 
7.7%
i 7818
 
7.4%
n 7422
 
7.1%
s 7375
 
7.0%
o 7276
 
6.9%
t 6620
 
6.3%
l 5629
 
5.4%
d 4150
 
3.9%
Other values (33) 29603
28.2%
Common
ValueCountFrequency (%)
9739
98.6%
3 35
 
0.4%
1 26
 
0.3%
2 15
 
0.2%
9 10
 
0.1%
4 9
 
0.1%
8
 
0.1%
0 6
 
0.1%
7 6
 
0.1%
5 4
 
< 0.1%
Other values (13) 24
 
0.2%
Inherited
ValueCountFrequency (%)
̀ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114938
99.9%
None 82
 
0.1%
Punctuation 14
 
< 0.1%
Diacriticals 2
 
< 0.1%
Latin Ext Additional 2
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12071
 
10.5%
9739
 
8.5%
a 9109
 
7.9%
r 8082
 
7.0%
i 7818
 
6.8%
n 7422
 
6.5%
s 7375
 
6.4%
o 7276
 
6.3%
t 6620
 
5.8%
l 5629
 
4.9%
Other values (27) 33797
29.4%
None
ValueCountFrequency (%)
é 43
52.4%
á 5
 
6.1%
í 4
 
4.9%
ó 3
 
3.7%
è 3
 
3.7%
¢ 2
 
2.4%
³ 2
 
2.4%
¿ 2
 
2.4%
¡ 2
 
2.4%
à 2
 
2.4%
Other values (14) 14
 
17.1%
Punctuation
ValueCountFrequency (%)
8
57.1%
3
 
21.4%
3
 
21.4%
Diacriticals
ValueCountFrequency (%)
̀ 2
100.0%
Latin Ext Additional
ValueCountFrequency (%)
2
100.0%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%

VoteAverage
Real number (ℝ)

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5363936
Minimum4.4
Maximum8.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:53.126656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile5.2
Q16
median6.6
Q37.1
95-th percentile7.8
Maximum8.6
Range4.2
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.79971719
Coefficient of variation (CV)0.12234838
Kurtosis-0.33188337
Mean6.5363936
Median Absolute Deviation (MAD)0.6
Skewness-0.15914279
Sum55461.3
Variance0.63954758
MonotonicityNot monotonic
2023-11-06T13:33:53.371565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
6.5 424
 
5.0%
6.3 406
 
4.8%
6.6 399
 
4.7%
6.8 394
 
4.6%
7 392
 
4.6%
6.2 385
 
4.5%
6.7 381
 
4.5%
6.4 376
 
4.4%
6.9 374
 
4.4%
6.1 371
 
4.4%
Other values (33) 4583
54.0%
ValueCountFrequency (%)
4.4 22
 
0.3%
4.5 36
 
0.4%
4.6 39
 
0.5%
4.7 46
 
0.5%
4.8 48
 
0.6%
4.9 62
0.7%
5 79
0.9%
5.1 83
1.0%
5.2 118
1.4%
5.3 137
1.6%
ValueCountFrequency (%)
8.6 2
 
< 0.1%
8.5 10
 
0.1%
8.4 26
 
0.3%
8.3 39
 
0.5%
8.2 53
 
0.6%
8.1 60
 
0.7%
8 85
1.0%
7.9 110
1.3%
7.8 129
1.5%
7.7 153
1.8%

VoteCount
Real number (ℝ)

HIGH CORRELATION 

Distinct3247
Distinct (%)38.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3047836
Minimum0
Maximum10.452418
Zeros25
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:53.619954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2958369
Q15.3752784
median6.4281053
Q37.4312997
95-th percentile8.7396325
Maximum10.452418
Range10.452418
Interquartile range (IQR)2.0560213

Descriptive statistics

Standard deviation1.6499974
Coefficient of variation (CV)0.26170564
Kurtosis0.93822067
Mean6.3047836
Median Absolute Deviation (MAD)1.0254279
Skewness-0.69087337
Sum53496.089
Variance2.7224914
MonotonicityNot monotonic
2023-11-06T13:33:53.880803image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6931471806 30
 
0.4%
1.609437912 29
 
0.3%
1.386294361 27
 
0.3%
1.945910149 26
 
0.3%
2.302585093 25
 
0.3%
0 25
 
0.3%
2.079441542 24
 
0.3%
3.583518938 19
 
0.2%
4.700480366 19
 
0.2%
1.098612289 19
 
0.2%
Other values (3237) 8242
97.1%
ValueCountFrequency (%)
0 25
0.3%
0.6931471806 30
0.4%
1.098612289 19
0.2%
1.386294361 27
0.3%
1.609437912 29
0.3%
1.791759469 17
0.2%
1.945910149 26
0.3%
2.079441542 24
0.3%
2.197224577 18
0.2%
2.302585093 25
0.3%
ValueCountFrequency (%)
10.45241788 1
< 0.1%
10.19320505 1
< 0.1%
10.08434971 1
< 0.1%
10.01721794 1
< 0.1%
9.980448594 1
< 0.1%
9.957833682 1
< 0.1%
9.957549511 1
< 0.1%
9.951896692 1
< 0.1%
9.951277216 1
< 0.1%
9.937888979 1
< 0.1%

Budget
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct638
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5884023
Minimum0
Maximum19.519293
Zeros3567
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:54.150016image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.068274
Q317.034386
95-th percentile18.292847
Maximum19.519293
Range19.519293
Interquartile range (IQR)17.034386

Descriptive statistics

Standard deviation8.2607972
Coefficient of variation (CV)0.86154052
Kurtosis-1.8691703
Mean9.5884023
Median Absolute Deviation (MAD)3.0647251
Skewness-0.26417348
Sum81357.594
Variance68.24077
MonotonicityNot monotonic
2023-11-06T13:33:54.419195image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3567
42.0%
16.81124283 192
 
2.3%
17.21670794 172
 
2.0%
17.03438638 165
 
1.9%
16.11809565 159
 
1.9%
16.52356076 148
 
1.7%
17.50439001 142
 
1.7%
15.42494847 139
 
1.6%
17.72753356 128
 
1.5%
17.37085862 121
 
1.4%
Other values (628) 3552
41.9%
ValueCountFrequency (%)
0 3567
42.0%
1.386294361 1
 
< 0.1%
1.609437912 1
 
< 0.1%
1.791759469 1
 
< 0.1%
1.945910149 1
 
< 0.1%
2.995732274 1
 
< 0.1%
3.258096538 1
 
< 0.1%
3.555348061 1
 
< 0.1%
4.418840608 1
 
< 0.1%
4.48863637 1
 
< 0.1%
ValueCountFrequency (%)
19.51929303 1
 
< 0.1%
19.33697148 6
 
0.1%
19.31676877 2
 
< 0.1%
19.25358987 1
 
< 0.1%
19.23161096 3
 
< 0.1%
19.18614859 1
 
< 0.1%
19.15784481 1
 
< 0.1%
19.13852054 1
 
< 0.1%
19.11382792 32
0.4%
19.08851012 1
 
< 0.1%
Distinct6567
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:54.938869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length148
Median length106
Mean length20.492398
Min length0

Characters and Unicode

Total characters173878
Distinct characters53
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6447 ?
Unique (%)76.0%

Sample

1st rowmind scene crime
2nd rowworld secrets legacy
3rd rowwelcome real world
4th rowai combat warrior unleashed
5th rowhappiest movie ever
ValueCountFrequency (%)
one 458
 
1.6%
love 322
 
1.2%
never 265
 
1.0%
story 259
 
0.9%
world 207
 
0.7%
life 206
 
0.7%
time 179
 
0.6%
man 178
 
0.6%
back 177
 
0.6%
get 174
 
0.6%
Other values (5591) 25366
91.3%
2023-11-06T13:33:56.296247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 22037
12.7%
21074
12.1%
t 10964
 
6.3%
r 10956
 
6.3%
n 10716
 
6.2%
a 10629
 
6.1%
s 10561
 
6.1%
o 10201
 
5.9%
i 9940
 
5.7%
l 7703
 
4.4%
Other values (43) 49097
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 152551
87.7%
Space Separator 21074
 
12.1%
Final Punctuation 96
 
0.1%
Other Punctuation 88
 
0.1%
Decimal Number 55
 
< 0.1%
Dash Punctuation 10
 
< 0.1%
Initial Punctuation 3
 
< 0.1%
Modifier Letter 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22037
14.4%
t 10964
 
7.2%
r 10956
 
7.2%
n 10716
 
7.0%
a 10629
 
7.0%
s 10561
 
6.9%
o 10201
 
6.7%
i 9940
 
6.5%
l 7703
 
5.0%
d 6153
 
4.0%
Other values (24) 42691
28.0%
Decimal Number
ValueCountFrequency (%)
1 11
20.0%
3 11
20.0%
2 10
18.2%
0 6
10.9%
9 5
9.1%
6 4
 
7.3%
8 3
 
5.5%
4 2
 
3.6%
5 2
 
3.6%
7 1
 
1.8%
Final Punctuation
ValueCountFrequency (%)
94
97.9%
2
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
6
60.0%
4
40.0%
Initial Punctuation
ValueCountFrequency (%)
2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
21074
100.0%
Other Punctuation
ValueCountFrequency (%)
88
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 152551
87.7%
Common 21327
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22037
14.4%
t 10964
 
7.2%
r 10956
 
7.2%
n 10716
 
7.0%
a 10629
 
7.0%
s 10561
 
6.9%
o 10201
 
6.7%
i 9940
 
6.5%
l 7703
 
5.0%
d 6153
 
4.0%
Other values (24) 42691
28.0%
Common
ValueCountFrequency (%)
21074
98.8%
94
 
0.4%
88
 
0.4%
1 11
 
0.1%
3 11
 
0.1%
2 10
 
< 0.1%
6
 
< 0.1%
0 6
 
< 0.1%
9 5
 
< 0.1%
4
 
< 0.1%
Other values (9) 18
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173666
99.9%
Punctuation 197
 
0.1%
None 14
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 22037
12.7%
21074
12.1%
t 10964
 
6.3%
r 10956
 
6.3%
n 10716
 
6.2%
a 10629
 
6.1%
s 10561
 
6.1%
o 10201
 
5.9%
i 9940
 
5.7%
l 7703
 
4.4%
Other values (27) 48885
28.1%
Punctuation
ValueCountFrequency (%)
94
47.7%
88
44.7%
6
 
3.0%
4
 
2.0%
2
 
1.0%
2
 
1.0%
1
 
0.5%
None
ValueCountFrequency (%)
é 4
28.6%
á 2
14.3%
ñ 2
14.3%
ü 2
14.3%
ō 1
 
7.1%
í 1
 
7.1%
ù 1
 
7.1%
ê 1
 
7.1%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%

RunTime
Real number (ℝ)

Distinct97
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.65681
Minimum55
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:56.592588image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile79
Q192
median102
Q3115
95-th percentile134
Maximum151
Range96
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.65299
Coefficient of variation (CV)0.16065506
Kurtosis-0.048322047
Mean103.65681
Median Absolute Deviation (MAD)11
Skewness0.3298986
Sum879528
Variance277.32209
MonotonicityNot monotonic
2023-11-06T13:33:56.870799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 269
 
3.2%
90 259
 
3.1%
100 243
 
2.9%
93 237
 
2.8%
105 229
 
2.7%
97 228
 
2.7%
98 226
 
2.7%
94 214
 
2.5%
101 214
 
2.5%
96 211
 
2.5%
Other values (87) 6155
72.5%
ValueCountFrequency (%)
55 3
 
< 0.1%
56 4
 
< 0.1%
57 3
 
< 0.1%
58 4
 
< 0.1%
59 7
0.1%
60 15
0.2%
61 7
0.1%
62 6
 
0.1%
63 7
0.1%
64 7
0.1%
ValueCountFrequency (%)
151 12
0.1%
150 12
0.1%
149 13
0.2%
148 10
0.1%
147 18
0.2%
146 15
0.2%
145 21
0.2%
144 13
0.2%
143 24
0.3%
142 14
0.2%

Revenue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5063
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.575197
Minimum0
Maximum21.449956
Zeros3212
Zeros (%)37.9%
Negative0
Negative (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:57.142160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.588335
Q317.82339
95-th percentile19.403895
Maximum21.449956
Range21.449956
Interquartile range (IQR)17.82339

Descriptive statistics

Standard deviation8.427223
Coefficient of variation (CV)0.79688566
Kurtosis-1.7346242
Mean10.575197
Median Absolute Deviation (MAD)3.3351505
Skewness-0.39258084
Sum89730.548
Variance71.018087
MonotonicityNot monotonic
2023-11-06T13:33:57.654203image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3212
37.9%
16.21340583 11
 
0.1%
14.50865774 10
 
0.1%
16.30041721 10
 
0.1%
15.76142071 8
 
0.1%
16.11809565 8
 
0.1%
17.03438638 8
 
0.1%
17.21670794 8
 
0.1%
15.42494847 7
 
0.1%
15.8949521 6
 
0.1%
Other values (5053) 5197
61.2%
ValueCountFrequency (%)
0 3212
37.9%
1.098612289 1
 
< 0.1%
1.945910149 1
 
< 0.1%
2.302585093 1
 
< 0.1%
3.36729583 1
 
< 0.1%
3.761200116 1
 
< 0.1%
4.543294782 1
 
< 0.1%
4.836281907 1
 
< 0.1%
5.303304908 1
 
< 0.1%
5.733341277 1
 
< 0.1%
ValueCountFrequency (%)
21.44995592 1
< 0.1%
21.23700967 1
< 0.1%
21.1389066 1
< 0.1%
21.02331567 1
< 0.1%
20.99364886 1
< 0.1%
20.95921976 1
< 0.1%
20.94063705 1
< 0.1%
20.93515034 1
< 0.1%
20.91848487 1
< 0.1%
20.86886373 1
< 0.1%

Genres
Text

Distinct2079
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
2023-11-06T13:33:57.918113image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length76
Median length53
Mean length19.871656
Min length3

Characters and Unicode

Total characters168611
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1307 ?
Unique (%)15.4%

Sample

1st rowaction sciencefiction adventure
2nd rowaction adventure sciencefiction
3rd rowaction sciencefiction
4th rowsciencefiction
5th rowfamily animation comedy fantasy adventure music
ValueCountFrequency (%)
drama 3299
14.7%
comedy 2648
11.8%
thriller 2383
10.6%
action 2349
10.5%
adventure 1584
 
7.1%
romance 1372
 
6.1%
horror 1355
 
6.0%
crime 1193
 
5.3%
fantasy 1117
 
5.0%
family 1117
 
5.0%
Other values (9) 4032
18.0%
2023-11-06T13:33:58.501935image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 17881
10.6%
a 17604
10.4%
e 14212
 
8.4%
13964
 
8.3%
i 12914
 
7.7%
m 11902
 
7.1%
o 11732
 
7.0%
t 11027
 
6.5%
c 11012
 
6.5%
n 10778
 
6.4%
Other values (9) 35585
21.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 154647
91.7%
Space Separator 13964
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 17881
11.6%
a 17604
11.4%
e 14212
9.2%
i 12914
8.4%
m 11902
7.7%
o 11732
7.6%
t 11027
 
7.1%
c 11012
 
7.1%
n 10778
 
7.0%
d 7625
 
4.9%
Other values (8) 27960
18.1%
Space Separator
ValueCountFrequency (%)
13964
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 154647
91.7%
Common 13964
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 17881
11.6%
a 17604
11.4%
e 14212
9.2%
i 12914
8.4%
m 11902
7.7%
o 11732
7.6%
t 11027
 
7.1%
c 11012
 
7.1%
n 10778
 
7.0%
d 7625
 
4.9%
Other values (8) 27960
18.1%
Common
ValueCountFrequency (%)
13964
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 168611
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 17881
10.6%
a 17604
10.4%
e 14212
 
8.4%
13964
 
8.3%
i 12914
 
7.7%
m 11902
 
7.1%
o 11732
 
7.0%
t 11027
 
6.5%
c 11012
 
6.5%
n 10778
 
6.4%
Other values (9) 35585
21.1%

North America
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
1
6113 
0
2372 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8485
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 6113
72.0%
0 2372
 
28.0%

Length

2023-11-06T13:33:58.742668image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T13:33:58.960477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6113
72.0%
0 2372
 
28.0%

Most occurring characters

ValueCountFrequency (%)
1 6113
72.0%
0 2372
 
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8485
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6113
72.0%
0 2372
 
28.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6113
72.0%
0 2372
 
28.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6113
72.0%
0 2372
 
28.0%

Europe
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
0
6094 
1
2391 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8485
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6094
71.8%
1 2391
 
28.2%

Length

2023-11-06T13:33:59.144619image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T13:33:59.359518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6094
71.8%
1 2391
 
28.2%

Most occurring characters

ValueCountFrequency (%)
0 6094
71.8%
1 2391
 
28.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8485
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6094
71.8%
1 2391
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6094
71.8%
1 2391
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6094
71.8%
1 2391
 
28.2%

Asia
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
0
7083 
1
1402 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8485
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 7083
83.5%
1 1402
 
16.5%

Length

2023-11-06T13:33:59.558475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T13:33:59.775312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7083
83.5%
1 1402
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 7083
83.5%
1 1402
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8485
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7083
83.5%
1 1402
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7083
83.5%
1 1402
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7083
83.5%
1 1402
 
16.5%

Oceania
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
0
8285 
1
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8485
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8285
97.6%
1 200
 
2.4%

Length

2023-11-06T13:33:59.977571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T13:34:00.211887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8285
97.6%
1 200
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 8285
97.6%
1 200
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8485
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8285
97.6%
1 200
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8285
97.6%
1 200
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8285
97.6%
1 200
 
2.4%

South America
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
0
8384 
1
 
101

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8485
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8384
98.8%
1 101
 
1.2%

Length

2023-11-06T13:34:00.398518image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T13:34:00.628288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8384
98.8%
1 101
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 8384
98.8%
1 101
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8485
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8384
98.8%
1 101
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8384
98.8%
1 101
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8384
98.8%
1 101
 
1.2%

Africa
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size132.6 KiB
0
8419 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8485
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8419
99.2%
1 66
 
0.8%

Length

2023-11-06T13:34:00.823205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-06T13:34:01.075891image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8419
99.2%
1 66
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 8419
99.2%
1 66
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8485
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8419
99.2%
1 66
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8419
99.2%
1 66
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8419
99.2%
1 66
 
0.8%

Year
Real number (ℝ)

Distinct100
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.3326
Minimum1920
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.4 KiB
2023-11-06T13:34:01.318250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile1974
Q11999
median2011
Q32018
95-th percentile2022
Maximum2023
Range103
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.003821
Coefficient of variation (CV)0.007976654
Kurtosis2.7653366
Mean2006.3326
Median Absolute Deviation (MAD)9
Skewness-1.5394996
Sum17023732
Variance256.12228
MonotonicityNot monotonic
2023-11-06T13:34:01.588413image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 542
 
6.4%
2018 403
 
4.7%
2021 390
 
4.6%
2023 389
 
4.6%
2019 388
 
4.6%
2017 356
 
4.2%
2020 351
 
4.1%
2016 316
 
3.7%
2015 279
 
3.3%
2014 275
 
3.2%
Other values (90) 4796
56.5%
ValueCountFrequency (%)
1920 1
 
< 0.1%
1921 1
 
< 0.1%
1922 1
 
< 0.1%
1925 2
< 0.1%
1927 2
< 0.1%
1928 1
 
< 0.1%
1930 1
 
< 0.1%
1931 4
< 0.1%
1932 3
< 0.1%
1933 3
< 0.1%
ValueCountFrequency (%)
2023 389
4.6%
2022 542
6.4%
2021 390
4.6%
2020 351
4.1%
2019 388
4.6%
2018 403
4.7%
2017 356
4.2%
2016 316
3.7%
2015 279
3.3%
2014 275
3.2%

Interactions

2023-11-06T13:33:44.422473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:34.469058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:36.128625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:37.745076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:39.427601image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:41.187832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:42.679014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:44.676075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:34.719153image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:36.388690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:37.991105image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:39.683423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:41.427140image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:42.924163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:44.891248image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:34.943827image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:36.589755image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:38.181245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:39.897808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:41.617116image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:43.128035image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:45.162912image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:35.186010image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:36.801820image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:38.406374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:40.133884image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:41.839832image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:43.340965image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:45.413083image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:35.439690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:37.048526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:38.636086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:40.400089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:42.076995image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:43.789138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:45.628298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:35.673250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:37.269133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:38.986700image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:40.642848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:42.274769image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:44.001257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:45.841819image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:35.898866image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:37.465623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:39.214458image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:40.882184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:42.475859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-06T13:33:44.200343image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-11-06T13:34:01.815070image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
PopularityVoteAverageVoteCountBudgetRunTimeRevenueYearOriginalLanguageNorth AmericaEuropeAsiaOceaniaSouth AmericaAfrica
Popularity1.0000.1270.3870.2500.0650.2930.1370.0790.0990.0490.0330.0130.0000.000
VoteAverage0.1271.0000.237-0.0250.2860.083-0.0520.1920.1340.0270.1410.0000.0250.030
VoteCount0.3870.2371.0000.6710.2880.721-0.2470.4230.4210.0500.2890.0300.0740.034
Budget0.250-0.0250.6711.0000.3110.781-0.2490.3680.3800.0240.2100.0000.0580.000
RunTime0.0650.2860.2880.3111.0000.323-0.0560.0900.0670.1170.1380.0000.0000.022
Revenue0.2930.0830.7210.7810.3231.000-0.3020.3170.3340.1210.1440.0650.0530.000
Year0.137-0.052-0.247-0.249-0.056-0.3021.0000.1580.1910.1360.1100.0320.0530.022
OriginalLanguage0.0790.1920.4230.3680.0900.3170.1581.0000.7850.1470.5680.0710.1220.030
North America0.0990.1340.4210.3800.0670.3340.1910.7851.0000.2930.4850.0170.0870.034
Europe0.0490.0270.0500.0240.1170.1210.1360.1470.2931.0000.1760.0000.0000.024
Asia0.0330.1410.2890.2100.1380.1440.1100.5680.4850.1761.0000.0220.0250.020
Oceania0.0130.0000.0300.0000.0000.0650.0320.0710.0170.0000.0221.0000.0000.013
South America0.0000.0250.0740.0580.0000.0530.0530.1220.0870.0000.0250.0001.0000.000
Africa\r\r\r0.0000.0300.0340.0000.0220.0000.0220.0300.0340.0240.0200.0130.0001.000

Missing values

2023-11-06T13:33:46.193571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-06T13:33:46.788496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OriginalLanguageOverviewPopularityReleaseDateTitleVoteAverageVoteCountBudgetTagLineRunTimeRevenueGenresNorth AmericaEuropeAsiaOceaniaSouth AmericaAfricaYear
Id
272051cobb skilled thief commits corporate espionage infiltrating subconscious targets offered chance regain old life payment task considered impossible inception implantation another persons idea targets subconscious4.4012162010-07-15inception8.410.45241818.890684mind scene crime14820.531540action sciencefiction adventure1100002010
4976981natasha romanoff also known black widow confronts darker parts ledger dangerous conspiracy ties past arises pursued force stop nothing bring natasha must deal history spy broken relationships left wake long became avenger4.3995962021-07-07black widow7.39.14291819.113828world secrets legacy13419.755027action adventure sciencefiction1000002021
6031set 22nd century matrix tells story computer hacker joins group underground insurgents fighting vast powerful computers rule earth4.3939801999-03-30matrix8.210.08435017.958645welcome real world13619.954354action sciencefiction1000001999
8437940uninhabitable 22ndcentury earth outcome civil war hinges cloning brain elite soldier create robot mercenary4.3938192023-01-12junge6.26.2383250.000000ai combat warrior unleashed980.000000sciencefiction0010002023
4468931queen poppy branch make surprising discovery — troll worlds beyond distinct differences create big clashes various tribes mysterious threat puts trolls across land danger poppy branch band friends must embark epic quest create harmony among feuding trolls unite certain doom4.3934362020-03-11trolls world tour7.37.56734618.315320happiest movie ever9017.712964family animation comedy fantasy adventure music1000002020
7298540world reduced rubble massive earthquake one knows sure far ruins stretch cause earthquake may heart seoul one apartment building left standing called hwang gung apartments4.3908622023-08-09concrete utopia7.62.7080500.000000believe chosen1300.000000thriller sciencefiction drama0010002023
240211bella finds surrounded danger seattle ravaged string mysterious killings malicious vampire continues quest revenge midst forced choose love edward friendship jacob knowing decision potential ignite ageless struggle vampire werewolf graduation quickly approaching bella confronted important decision life4.3859072010-06-23twilight saga eclipse6.29.01893818.035018begins choice12420.364433adventure fantasy drama romance1000002010
18801dawn world war iii midwestern america group teenagers band together defend town—and country—from invading soviet forces4.3849721984-08-10red dawn6.36.55250816.648724time foreign army ever occupied american soil11417.462956action thriller war drama1000001984
42571cindy finds house lives haunted little boy goes quest find killed also alien tripods invading world uncover secret order stop4.3849352006-04-12scary movie5.58.00770017.622173bury grudge burn village see saw8318.998768comedy1000002006
286760young woman seeks vengeance finds love parents killed amazon taken prisoner indigenous tribe headhunters4.3835751985-08-09amazonia catherine miles story6.15.0369530.000000one thing kept alive900.000000adventure drama horror0100001985
OriginalLanguageOverviewPopularityReleaseDateTitleVoteAverageVoteCountBudgetTagLineRunTimeRevenueGenresNorth AmericaEuropeAsiaOceaniaSouth AmericaAfricaYear
Id
110921rusty sabich deputy prosecutor engaged obsessive affair coworker murdered soon hes accused crime fight clear name becomes whirlpool lies hidden passions2.5690951990-07-27presumed innocent6.86.38856116.906553people would kill love12719.215044mystery crime thriller1000001990
6644231story pioneering project rehabilitate child survivors holocaust shores lake windermere2.5689412020-01-27windermere children7.54.5643480.000000880.000000drama tvmovie history0100002020
30771one sons late dr henry frankenstein finds fathers ghoulish creation coma revives find monster controlled ygor bent revenge2.5689411939-01-13son frankenstein6.75.32301012.948010black shadows past bred halfman halfdemon creating new terrible juggernaut destruction990.000000horror sciencefiction1000001939
4135430unconventional thinker helps budding cinematographer gain new perspective life2.5688652016-11-23dear zindagi7.15.34710815.27412615115.032313drama romance0010002016
144000powerful billionaire murdered secret adoptive son must race prove legitimacy find fathers killers stop taking financial empire2.5688652008-12-17heir apparent largo winch6.06.18620917.0507621080.000000adventure drama action thriller0110002008
27491eastern european criminals oleg emil come new york city pick share heist score oleg steals video camera starts filming activities legal illegal learn american media circus make remorseless killer look like victim make rich target mediasavvy nypd homicide detective eddie flemming medianaive fdny fire marshal jordy warsaw cops investigating murder torching former criminal partner filming everything sell local tabloid tv show top story2.5688652001-03-01minutes5.96.47080017.909855america likes watch12017.847270action crime thriller1100002001
111281watchful eye mentor captain mike kennedy probationary firefighter jack morrison matures seasoned veteran baltimore fire station however jack reached crossroads sacrifices hes made put harms way innumerable times significantly impacted relationship wife kids2.5687882004-10-01ladder6.46.56103117.909855greatest challenge lies rescuing one11518.126869drama action thriller1000002004
484482040yearold bertrand suffering depression last two years barely able keep head water despite medication gulps day every day wifes encouragement unable find meaning life curiously end finding sense purpose swimming pool joining allmale synchronised swimming team2.5687122018-10-24sink swim6.97.2427980.0000001220.000000drama comedy0100002018
4537551man stranded arctic finally receive long awaited rescue however tragic accident opportunity lost must decide whether remain relative safety camp embark deadly trek unknown potential salvation2.5687122018-11-21arctic6.56.99851014.508658survival option9815.226498drama1100002018
545181tells story justin bieber kid canada hair smile voice chronicles unprecedented rise fame way busking streets stratford canada putting videos youtube selling madison square garden new york headline act world tour features usher scooter braun ludacris sean kingston antonio la reid boyz ii men miley cyrus jaden smith justins family members parts crew huge fanbase mix interviews guest performances released 3d theaters around world highest grossing concert movie time beating previous record held michael jacksons2.5687122011-02-11justin bieber never say never5.25.93489416.380460find whats possible never give10518.405567music documentary family1000002011